Data Mining and R Language

Original Title: Data Mining with R: learning with case studies Author: (Portuguese) Lu ís torgo Translator: Li Hongcheng Chen daolun Wu liming series name: computer Science Series Publishing House: Mechanical Industry Publishing House ISBN: 9787111407003 Release Date: April 2013 publication date: 16 open pages: 1: 1-1 category: Computer> database storage and management
For more information, data mining and R language introduction computer books data mining and R language first briefly introduces the basic knowledge of R Software (installation, r data structure, r programming, r input and output ). Then, we will introduce the data mining technology through four actual data mining cases (algae frequency prediction, securities trend prediction and transaction system simulation, transaction fraud prediction, and micro-array data classification. These four cases cover common data mining technologies, from unsupervised data mining technology, supervised data mining technology to semi-supervised data mining technology. The book organizes content based on actual problems, solutions, and discussions on solutions. The context is clear and the chapters are self-contained. You can start from the beginning to the end, or learn based on your needs to find your own solutions to actual problems. Data Mining and R language does not require readers to have basic R and Data Mining knowledge. R beginners and skilled R users can find useful content from their books. Readers can use data mining and R language as an excellent teaching material to learn how to apply R, or as a tool for data mining. Table of contents "Data Mining and R language" publisher's words recommended preface Chinese edition preface Thank you chapter 1st Introduction 1.1 how to read "Data Mining and R language" 1.2r introduction 1.2.1r start 1.2.2r object 1.2.3 vector 1.2.4 generate a series 1.2.7 data subset 1.2.8 matrix and an array 1.2.9 list 1.2.10 Data box by vectorizing factor 1.2.5 1.2.6. 1.2.11 build new functions 1.2.12 objects, classes and Methods 1.2.13 manage R sessions 1.3mysql Introduction Chapter 2nd predict seaweed count 2.1 Problem description and Target 2.2 Data Description 2.3 data loaded into r2.4 data visualization and summary 2.5 data missing 2.5.1 remove missing part 2.5.2 use the highest frequency value to fill the missing value 2.5.3 use the correlation between variables to fill the missing value 2.5.4 use case similarity to fill the missing value 2.6 to get the Prediction Model 2.6.1 multivariate linear regression 2.6.2 regression tree 2.7 model evaluation and selection 2.8 prediction 7 seaweed frequency 2.9 summary chapter 3rd prediction stock market profit 3.1 Problem description and Target 3.2 available data 3.2.1 In R processing time-related data 3.2.2 reading data from the CSV file 3.2.3 getting data from the website 3.2.4 reading data from the MySQL database 3.3 defining the prediction task 3.3.1 predicting what 3.3.2 the prediction variable is what 3.3.3 The prediction task 3.3.4 model evaluation criteria 3.4 prediction model 3.4.1 how to apply training set data for modeling 3.4.2 modeling tools 3.5 How to Apply prediction model 3.5.2 from prediction to practice 3.5.1 integration with transaction-related evaluation criteria 3.5.3 model: simulation transaction 3.6 model evaluation and selection 3.6.1 Monte Carlo estimation 3.6.2 experiment comparison 3.6.3 Result Analysis 3.7 transaction system 3.7.1 evaluation final test data 3.7.2 Online Transaction System 3.8 summary chapter 4th fraud transaction detection 4.1 Problem description and objective 4.2 loading data from available data 4.2.1 to r4.2.2 exploring dataset 4.2.3 data problem 4.3 defining different solutions for data mining Task 4.3.1 4.3.2 evaluation criteria 4.3.3 experiment method 4.4 calculating the sorting of departure values 4.4.1 unsupervised method 4.4.2 supervised method 4.4.3 semi-supervised method 4.5 summary chapter 5th micro-array sample classification 5.1 Problem description and objective 5.1.1 micro-array experiment background 5.1.2 dataset all5.2 available data 5.3 genes (features) 5.3.1 Simple distributed feature-based filtering method 5.3.2anova filtering 5.3.3 random forest filtering 5.3.4 feature clustering combination filtering 5.4 genetic exception prediction 5.4.1 definition prediction task 5.4.2 model evaluation standard 5.4.3 Experiment process 5.4.4 modeling technology 5.4.5 model comparison 5.5 summary references topic index data mining term index R function index Book Information Source: china Interactive publishing network